1. dV/dt - Accelerating the Rate of Progress towards Extreme Scale Collaborative Science DOE: Scientific Collaborations at Extreme-Scales:

2. 3 year project Lead Institution: University of Wisconsin - Madison Lead PI: Miron Livny Principal Investigators and Collaborating Institutions: Frank Wuerthwein, University of California–San Diego Douglas Thain, University of Notre Dame Ewa Deelman, University of Southern California William Allcock – Co-PI, U-Chicago Argonne, Argonne National Laboratory

3. Thesis Researchers band together into dynamic collaborations and employ a number of applications, software tools, data sources, and instruments They have access to a growing variety of processing, storage and networking resources Goal: “make it easier for scientists to conduct large-scale computational tasks that use the power of computing resources they do not own to process data they did not collect with applications they did not develop”

4. Challenges today finding the appropriate computing resources acquiring those resources deploying the applications and data on the resources managing applications as they run.

5. Approach A planning framework that is based on a well- defined trust model and covers the entire spectrum of computing resources— processing, storage, networking, and software The framework will encompass the four phases of collaborative computing—find, acquire, deploy, and use

6. Experimental Foundation Real-world applications State of the art computing capabilities—ALCF and OSG Campus resources at ND, UCSD and UW Commercial cloud services Experimentation from the point of view of a collaboration member: “submit locally and compute globally” Pay attention to the cost involved in acquiring the resources and the human effort involved in software and data deployment and application management

7. Computer Science Research Capacity planning, resource monitoring, provisioning, data placement, workload scheduling, and trust Extend existing algorithms and innovate Evaluate the solutions through simulation and large-scale experimentation – Make use of overlay software

8. Plans Identify key, challenging applications Develop a framework that allows to characterize the application needs, the resource availability, and plan for their use within a collaboration's goals Main threads of research: – Overall planning (dynamic) – Storage and data management – Network management – CPU/Core management Umbrella of a trust framework between the collaborating entities

9. Rough Schedule Year 1: Development of models of collaboration entities and set up the experimental infrastructure (focusing on processing) Year 2: Focusing on Data Management Year 3: Focusing on Network Management